Dajun Sun

l 0-norm penalised shrinkage linear and widely linear LMS algorithms for sparse system identification

In this study, the authors propose an l 0-norm penalised shrinkage linear least mean squares (l 0-SH-LMS) algorithm and an l 0-norm penalised shrinkage widely linear least mean squares (l 0-SH-WL-LMS) algorithm for sparse system identification. The proposed algorithms exploit the priori and the posteriori errors to calculate the varying step-size, thus they can adapt to the time-varying channel. Meanwhile, in the cost function they introduce a penalty term that favours sparsity to enable the applicability for sparse condition. Moreover, the l 0-SH-WL-LMS algorithm also makes full use of the non-circular properties of the signals of interest to improve the tracking capability and estimation performance. Quantitative analysis of the convergence behaviour for the l 0-SH-WL-LMS algorithm verifies the capabilities of the proposed algorithms. Simulation results show that compared with the existing least mean squares-type algorithms, the proposed algorithms perform better in the sparse channels with a faster convergence rate and a lower steady-state error. When channel changes suddenly, a filter with the proposed algorithms can adapt to the variation of the channel quickly.

Measurement Error Impact on Node Localization of Large Scale Underwater Sensor Networks

This paper studies the impact of distance measurement error on node localization in large scale Underwater Wireless Sensor Networks (UWSNs) where only a small percent of nodes are anchor nodes with accurate location information and a large percent of ordinary nodes are to be localized. The recursive localization algorithm is re-evaluated under realistic assumptions that low-cost underwater sensor node may experience large system errors and random errors of distance measurement. Our results show that the errors indeed cause severe performance degradation in localization coverage and normalized localization errors. The sources of distance measurement errors are also identified and their remedies are suggested.

Low-Complexity l_0-Norm Penalized Shrinkage Linear and Widely Linear Affine Projection Algorithms

In this paper, we propose an

Gridless SPICE applied to parameter estimation of underwater acoustic Frequency Hopping signals

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Geoacoustic inversion of the pressure gradient data using a single vector sensor

l0-norm penalized shrinkage linear affine projection (

Low complexity OMP based matched-phase coherent shallow water broadband multi-source localization

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